The Grey Wolf Optimizer (GWO) was easy to fall into the problem of the imbalance of local optimum, global search ability and local development ability, a hybrid gray wolf algorithm DBGWO based on dance behavior was proposed under nonlinear convergence factor strategy. Firstly, a nonlinear convergence factor was proposed to balance the global search ability and local development ability, and secondly, the dance behavior of Dung Beetle Optimizer (DBO) was introduced, in order to improve the convergence accuracy and global optimization ability of the algorithm, the gray wolf individual could jump out of the current moving mode with a certain probability and adjust the diversity of population. Compared with other swarm intelligence algorithms and improved gray wolf algorithm, the experiment was carried out on 12 benchmark functions. The superiority of DBGWO algorithm is verified in many aspects Compared with the original grey wolf algorithm, the proposed algorithm is proved to be stable and effective in dealing with high-dimensional complex problems, the experimental results of wireless network coverage optimization also further prove that DBGWO algorithm is highly efficient.